1 Libraries


  library(package = "tidyverse")
  library(package = "lubridate")

  library(package = "rlist")
  
  library(package = "ncdf4")
  library(package = "ncdf4.helpers")
  
  library(package = "PCICt")
  
  library(package = "reshape2")

2 Input Data File


# directory/URL root

  directory = "/projects/BIG_WEATHER/GENS_ERROR_RT/triangle_archives/"
  directory = "http://kyrill.ias.sdsmt.edu:8080/thredds/fileServer/BWW_GENS/CI_STAT/"

  time_range = "2015-06-01_00_to_2019-03-31_00"
  
  region = "WRFRAP"
  
  RdataFile = str_c(directory,
                  "gens_03_ensemble__",
                  "T2M_MSLP_M10_SPCH2M_ISOHGT_U10_V10_FRICV_GUST",
                  "__error__",
                  region,
                  "__",
                  time_range,
                  ".RData",
                  sep = "")

  load(file = url(RdataFile)) 
  
  Time     = unique(CI_Ensemble_Stats$Time)
  Fx_Hour  = unique(CI_Ensemble_Stats$Fx_Hour)
  Variable = unique(CI_Ensemble_Stats$Variable)
  Height   = unique(CI_Ensemble_Stats$Height)
  
  CI_Ensemble_Stats$Month   = month(CI_Ensemble_Stats$Time)

  CI_Ensemble_Stats = CI_Ensemble_Stats %>% 
    mutate(Quarter = case_when(((Month == 12) | (Month == 01) | (Month == 02))  
                               ~ "DJF",
                               ((Month == 03) | (Month == 04) | (Month == 05))
                               ~ "MAM",
                               ((Month == 06) | (Month == 07) | (Month == 08))
                               ~ "JJA",
                               ((Month == 09) | (Month == 10) | (Month == 11))
                               ~ "SON") )

3 Triangle Plots

3a 2-m Air Temperature


  Var     = "T2M"
  Varname = "2-m Air Temperature"
  Hgt     = 2
  Fx      = 24

  for (Fx in Fx_Hour[2:length(Fx_Hour)]) {
    
    fx_subset = CI_Ensemble_Stats %>% filter((Variable == Var) &
                                             (Height   == Hgt) &
                                             (Fx_Hour  == Fx)  ) %>%
                                      select(-Quarter)
    
    seasonal_subset = CI_Ensemble_Stats %>% filter((Variable == Var) &
                                                   (Height   == Hgt) &
                                                   (Fx_Hour  == Fx)  )
    
    myplot = ggplot(data = seasonal_subset) +
      
      aes(x       = Ens_StDev,
          y       = RMSE_Ens000,
          color   = Quarter) + 
      
      facet_wrap(facets = ~ Quarter) + 
      
      theme_bw() +
      
      theme(strip.background = element_rect(fill=NA),
            ) +
      
      labs(title    = str_c(Fx,
                            "-hr Forecast, CI Seasonal Triangles for ",
                            Varname,
                            sep = ""),
           subtitle = str_c(region_name,
                            sep = "")) +
        
      xlab("Ensemble Standard Deviation") + 
      
      ylab("Root Mean Squared Error") +
      
      geom_point(data    = fx_subset,
                 color   = "grey",
                 alpha   = 0.5) +
      
      scale_colour_manual(values=c("DJF" = "cyan",
                                   "MAM" = "green",
                                   "JJA" = "magenta",
                                   "SON" = "orange"),
                        guide =FALSE) +
    
      geom_point(data    = seasonal_subset,
                 alpha   = 0.7)

      
    print(myplot)
    
  }

3b 500-hPa Heights


  Var     = "ISOHGT"
  Varname = "500-hPa Isobaric Heights"
  Hgt     = 500 * 100
  Fx      = 24

  for (Fx in Fx_Hour[2:length(Fx_Hour)]) {
    
    fx_subset = CI_Ensemble_Stats %>% filter((Variable == Var) &
                                             (Height   == Hgt) &
                                             (Fx_Hour  == Fx)  ) %>%
                                      select(-Quarter)
    
    seasonal_subset = CI_Ensemble_Stats %>% filter((Variable == Var) &
                                                   (Height   == Hgt) &
                                                   (Fx_Hour  == Fx)  )
    
    myplot = ggplot(data = seasonal_subset) +
      
      aes(x       = Ens_StDev,
          y       = RMSE_Ens000,
          color   = Quarter) + 
      
      facet_wrap(facets = ~ Quarter) + 
      
      theme_bw() +
      
      theme(strip.background = element_rect(fill=NA),
            ) +
      
      labs(title    = str_c(Fx,
                            "-hr Forecast, CI Seasonal Triangles for ",
                            Varname,
                            sep = ""),
           subtitle = str_c(region_name,
                            sep = "")) +
        
      xlab("Ensemble Standard Deviation") + 
      
      ylab("Root Mean Squared Error") +
      
      geom_point(data    = fx_subset,
                 color   = "grey",
                 alpha   = 0.5) +
      
      scale_colour_manual(values=c("DJF" = "cyan",
                                   "MAM" = "green",
                                   "JJA" = "magenta",
                                   "SON" = "orange"),
                        guide =FALSE) +
    
      geom_point(data    = seasonal_subset,
                 alpha   = 0.7)

      
    print(myplot)
    
  }

3c Mean Sea Level Pressure


  Var     = "MSLP"
  Varname = "Mean Sea Level Pressure"
  Hgt     = 0
  Fx      = 24

  for (Fx in Fx_Hour[2:length(Fx_Hour)]) {
    
    fx_subset = CI_Ensemble_Stats %>% filter((Variable == Var) &
                                             (Height   == Hgt) &
                                             (Fx_Hour  == Fx)  ) %>%
                                      select(-Quarter)
    
    seasonal_subset = CI_Ensemble_Stats %>% filter((Variable == Var) &
                                                   (Height   == Hgt) &
                                                   (Fx_Hour  == Fx)  )
    
    myplot = ggplot(data = seasonal_subset) +
      
      aes(x       = Ens_StDev,
          y       = RMSE_Ens000,
          color   = Quarter) + 
      
      facet_wrap(facets = ~ Quarter) + 
      
      theme_bw() +
      
      theme(strip.background = element_rect(fill=NA),
            ) +
      
      labs(title    = str_c(Fx,
                            "-hr Forecast, CI Seasonal Triangles for ",
                            Varname,
                            sep = ""),
           subtitle = str_c(region_name,
                            sep = "")) +
        
      xlab("Ensemble Standard Deviation") + 
      
      ylab("Root Mean Squared Error") +
      
      geom_point(data    = fx_subset,
                 color   = "grey",
                 alpha   = 0.5) +
      
      scale_colour_manual(values=c("DJF" = "cyan",
                                   "MAM" = "green",
                                   "JJA" = "magenta",
                                   "SON" = "orange"),
                        guide =FALSE) +
    
      geom_point(data    = seasonal_subset,
                 alpha   = 0.7)

      
    print(myplot)
    
  }

3d 10-m Wind Speeds


  Var     = "M10"
  Varname = "10-m Wind Speed"
  Hgt     = 10
  Fx      = 24

  for (Fx in Fx_Hour[2:length(Fx_Hour)]) {
    
    fx_subset = CI_Ensemble_Stats %>% filter((Variable == Var) &
                                             (Height   == Hgt) &
                                             (Fx_Hour  == Fx)  ) %>%
                                      select(-Quarter)
    
    seasonal_subset = CI_Ensemble_Stats %>% filter((Variable == Var) &
                                                   (Height   == Hgt) &
                                                   (Fx_Hour  == Fx)  )
    
    myplot = ggplot(data = seasonal_subset) +
      
      aes(x       = Ens_StDev,
          y       = RMSE_Ens000,
          color   = Quarter) + 
      
      facet_wrap(facets = ~ Quarter) + 
      
      theme_bw() +
      
      theme(strip.background = element_rect(fill=NA),
            ) +
      
      labs(title    = str_c(Fx,
                            "-hr Forecast, CI Seasonal Triangles for ",
                            Varname,
                            sep = ""),
           subtitle = str_c(region_name,
                            sep = "")) +
        
      xlab("Ensemble Standard Deviation") + 
      
      ylab("Root Mean Squared Error") +
      
      geom_point(data    = fx_subset,
                 color   = "grey",
                 alpha   = 0.5) +
      
      scale_colour_manual(values=c("DJF" = "cyan",
                                   "MAM" = "green",
                                   "JJA" = "magenta",
                                   "SON" = "orange"),
                        guide =FALSE) +
    
      geom_point(data    = seasonal_subset,
                 alpha   = 0.7)

      
    print(myplot)
    
  }

3e 10-m Wind Gusts


  Var     = "GUST"
  Varname = "10-m Wind Gusts"
  Hgt     = 10
  Fx      = 24

  for (Fx in Fx_Hour[2:length(Fx_Hour)]) {
    
    fx_subset = CI_Ensemble_Stats %>% filter((Variable == Var) &
                                             (Height   == Hgt) &
                                             (Fx_Hour  == Fx)  ) %>%
                                      select(-Quarter)
    
    seasonal_subset = CI_Ensemble_Stats %>% filter((Variable == Var) &
                                                   (Height   == Hgt) &
                                                   (Fx_Hour  == Fx)  )
    
    myplot = ggplot(data = seasonal_subset) +
      
      aes(x       = Ens_StDev,
          y       = RMSE_Ens000,
          color   = Quarter) + 
      
      facet_wrap(facets = ~ Quarter) + 
      
      theme_bw() +
      
      theme(strip.background = element_rect(fill=NA)) +
      
      labs(title    = str_c(Fx,
                            "-hr Forecast, CI Seasonal Triangles for ",
                            Varname,
                            sep = ""),
           subtitle = str_c(region_name,
                            sep = "")) +
        
      xlab("Ensemble Standard Deviation") + 
      
      ylab("Root Mean Squared Error") + 
      
      geom_point(data    = fx_subset,
                 color   = "grey",
                 alpha   = 0.5) +
      
      scale_colour_manual(values=c("DJF" = "cyan",
                                   "MAM" = "green",
                                   "JJA" = "magenta",
                                   "SON" = "orange"),
                        guide =FALSE) +
    
      geom_point(data    = seasonal_subset,
                 alpha   = 0.7)

      
    print(myplot)
    
  }

---
title: "CI Extended Triangle Analysis"
output:
  html_notebook:
    toc: true
---

# 1 Libraries

```{r}

  library(package = "tidyverse")
  library(package = "lubridate")

  library(package = "rlist")
  
  library(package = "ncdf4")
  library(package = "ncdf4.helpers")
  
  library(package = "PCICt")
  
  library(package = "reshape2")
```

# 2 Input Data File

```{r}

# directory/URL root

  directory = "/projects/BIG_WEATHER/GENS_ERROR_RT/triangle_archives/"
  directory = "http://kyrill.ias.sdsmt.edu:8080/thredds/fileServer/BWW_GENS/CI_STAT/"

  time_range = "2015-06-01_00_to_2019-03-31_00"
  
  region = "WRFRAP"
  
  RdataFile = str_c(directory,
                  "gens_03_ensemble__",
                  "T2M_MSLP_M10_SPCH2M_ISOHGT_U10_V10_FRICV_GUST",
                  "__error__",
                  region,
                  "__",
                  time_range,
                  ".RData",
                  sep = "")

  load(file = url(RdataFile)) 
  
  Time     = unique(CI_Ensemble_Stats$Time)
  Fx_Hour  = unique(CI_Ensemble_Stats$Fx_Hour)
  Variable = unique(CI_Ensemble_Stats$Variable)
  Height   = unique(CI_Ensemble_Stats$Height)
  
  CI_Ensemble_Stats$Month   = month(CI_Ensemble_Stats$Time)

  CI_Ensemble_Stats = CI_Ensemble_Stats %>% 
    mutate(Quarter = case_when(((Month == 12) | (Month == 01) | (Month == 02))  
                               ~ "DJF",
                               ((Month == 03) | (Month == 04) | (Month == 05))
                               ~ "MAM",
                               ((Month == 06) | (Month == 07) | (Month == 08))
                               ~ "JJA",
                               ((Month == 09) | (Month == 10) | (Month == 11))
                               ~ "SON") )

```

# 3 Triangle Plots

## 3a 2-m Air Temperature

```{r}

  Var     = "T2M"
  Varname = "2-m Air Temperature"
  Hgt     = 2
  Fx      = 24

  for (Fx in Fx_Hour[2:length(Fx_Hour)]) {
    
    fx_subset = CI_Ensemble_Stats %>% filter((Variable == Var) &
                                             (Height   == Hgt) &
                                             (Fx_Hour  == Fx)  ) %>%
                                      select(-Quarter)
    
    seasonal_subset = CI_Ensemble_Stats %>% filter((Variable == Var) &
                                                   (Height   == Hgt) &
                                                   (Fx_Hour  == Fx)  )
    
    myplot = ggplot(data = seasonal_subset) +
      
      aes(x       = Ens_StDev,
          y       = RMSE_Ens000,
          color   = Quarter) + 
      
      facet_wrap(facets = ~ Quarter) + 
      
      theme_bw() +
      
      theme(strip.background = element_rect(fill=NA),
            ) +
      
      labs(title    = str_c(Fx,
                            "-hr Forecast, CI Seasonal Triangles for ",
                            Varname,
                            sep = ""),
           subtitle = str_c(region_name,
                            sep = "")) +
        
      xlab("Ensemble Standard Deviation") + 
      
      ylab("Root Mean Squared Error") +
      
      geom_point(data    = fx_subset,
                 color   = "grey",
                 alpha   = 0.5) +
      
      scale_colour_manual(values=c("DJF" = "cyan",
                                   "MAM" = "green",
                                   "JJA" = "magenta",
                                   "SON" = "orange"),
                        guide =FALSE) +
    
      geom_point(data    = seasonal_subset,
                 alpha   = 0.7)

      
    print(myplot)
    
  }

```

## 3b 500-hPa Heights

```{r}

  Var     = "ISOHGT"
  Varname = "500-hPa Isobaric Heights"
  Hgt     = 500 * 100
  Fx      = 24

  for (Fx in Fx_Hour[2:length(Fx_Hour)]) {
    
    fx_subset = CI_Ensemble_Stats %>% filter((Variable == Var) &
                                             (Height   == Hgt) &
                                             (Fx_Hour  == Fx)  ) %>%
                                      select(-Quarter)
    
    seasonal_subset = CI_Ensemble_Stats %>% filter((Variable == Var) &
                                                   (Height   == Hgt) &
                                                   (Fx_Hour  == Fx)  )
    
    myplot = ggplot(data = seasonal_subset) +
      
      aes(x       = Ens_StDev,
          y       = RMSE_Ens000,
          color   = Quarter) + 
      
      facet_wrap(facets = ~ Quarter) + 
      
      theme_bw() +
      
      theme(strip.background = element_rect(fill=NA),
            ) +
      
      labs(title    = str_c(Fx,
                            "-hr Forecast, CI Seasonal Triangles for ",
                            Varname,
                            sep = ""),
           subtitle = str_c(region_name,
                            sep = "")) +
        
      xlab("Ensemble Standard Deviation") + 
      
      ylab("Root Mean Squared Error") +
      
      geom_point(data    = fx_subset,
                 color   = "grey",
                 alpha   = 0.5) +
      
      scale_colour_manual(values=c("DJF" = "cyan",
                                   "MAM" = "green",
                                   "JJA" = "magenta",
                                   "SON" = "orange"),
                        guide =FALSE) +
    
      geom_point(data    = seasonal_subset,
                 alpha   = 0.7)

      
    print(myplot)
    
  }

```

## 3c Mean Sea Level Pressure

```{r}

  Var     = "MSLP"
  Varname = "Mean Sea Level Pressure"
  Hgt     = 0
  Fx      = 24

  for (Fx in Fx_Hour[2:length(Fx_Hour)]) {
    
    fx_subset = CI_Ensemble_Stats %>% filter((Variable == Var) &
                                             (Height   == Hgt) &
                                             (Fx_Hour  == Fx)  ) %>%
                                      select(-Quarter)
    
    seasonal_subset = CI_Ensemble_Stats %>% filter((Variable == Var) &
                                                   (Height   == Hgt) &
                                                   (Fx_Hour  == Fx)  )
    
    myplot = ggplot(data = seasonal_subset) +
      
      aes(x       = Ens_StDev,
          y       = RMSE_Ens000,
          color   = Quarter) + 
      
      facet_wrap(facets = ~ Quarter) + 
      
      theme_bw() +
      
      theme(strip.background = element_rect(fill=NA),
            ) +
      
      labs(title    = str_c(Fx,
                            "-hr Forecast, CI Seasonal Triangles for ",
                            Varname,
                            sep = ""),
           subtitle = str_c(region_name,
                            sep = "")) +
        
      xlab("Ensemble Standard Deviation") + 
      
      ylab("Root Mean Squared Error") +
      
      geom_point(data    = fx_subset,
                 color   = "grey",
                 alpha   = 0.5) +
      
      scale_colour_manual(values=c("DJF" = "cyan",
                                   "MAM" = "green",
                                   "JJA" = "magenta",
                                   "SON" = "orange"),
                        guide =FALSE) +
    
      geom_point(data    = seasonal_subset,
                 alpha   = 0.7)

      
    print(myplot)
    
  }

```

## 3d 10-m Wind Speeds

```{r}

  Var     = "M10"
  Varname = "10-m Wind Speed"
  Hgt     = 10
  Fx      = 24

  for (Fx in Fx_Hour[2:length(Fx_Hour)]) {
    
    fx_subset = CI_Ensemble_Stats %>% filter((Variable == Var) &
                                             (Height   == Hgt) &
                                             (Fx_Hour  == Fx)  ) %>%
                                      select(-Quarter)
    
    seasonal_subset = CI_Ensemble_Stats %>% filter((Variable == Var) &
                                                   (Height   == Hgt) &
                                                   (Fx_Hour  == Fx)  )
    
    myplot = ggplot(data = seasonal_subset) +
      
      aes(x       = Ens_StDev,
          y       = RMSE_Ens000,
          color   = Quarter) + 
      
      facet_wrap(facets = ~ Quarter) + 
      
      theme_bw() +
      
      theme(strip.background = element_rect(fill=NA),
            ) +
      
      labs(title    = str_c(Fx,
                            "-hr Forecast, CI Seasonal Triangles for ",
                            Varname,
                            sep = ""),
           subtitle = str_c(region_name,
                            sep = "")) +
        
      xlab("Ensemble Standard Deviation") + 
      
      ylab("Root Mean Squared Error") +
      
      geom_point(data    = fx_subset,
                 color   = "grey",
                 alpha   = 0.5) +
      
      scale_colour_manual(values=c("DJF" = "cyan",
                                   "MAM" = "green",
                                   "JJA" = "magenta",
                                   "SON" = "orange"),
                        guide =FALSE) +
    
      geom_point(data    = seasonal_subset,
                 alpha   = 0.7)

      
    print(myplot)
    
  }

```


## 3e 10-m Wind Gusts

```{r}

  Var     = "GUST"
  Varname = "10-m Wind Gusts"
  Hgt     = 10
  Fx      = 24

  for (Fx in Fx_Hour[2:length(Fx_Hour)]) {
    
    fx_subset = CI_Ensemble_Stats %>% filter((Variable == Var) &
                                             (Height   == Hgt) &
                                             (Fx_Hour  == Fx)  ) %>%
                                      select(-Quarter)
    
    seasonal_subset = CI_Ensemble_Stats %>% filter((Variable == Var) &
                                                   (Height   == Hgt) &
                                                   (Fx_Hour  == Fx)  )
    
    myplot = ggplot(data = seasonal_subset) +
      
      aes(x       = Ens_StDev,
          y       = RMSE_Ens000,
          color   = Quarter) + 
      
      facet_wrap(facets = ~ Quarter) + 
      
      theme_bw() +
      
      theme(strip.background = element_rect(fill=NA)) +
      
      labs(title    = str_c(Fx,
                            "-hr Forecast, CI Seasonal Triangles for ",
                            Varname,
                            sep = ""),
           subtitle = str_c(region_name,
                            sep = "")) +
        
      xlab("Ensemble Standard Deviation") + 
      
      ylab("Root Mean Squared Error") + 
      
      geom_point(data    = fx_subset,
                 color   = "grey",
                 alpha   = 0.5) +
      
      scale_colour_manual(values=c("DJF" = "cyan",
                                   "MAM" = "green",
                                   "JJA" = "magenta",
                                   "SON" = "orange"),
                        guide =FALSE) +
    
      geom_point(data    = seasonal_subset,
                 alpha   = 0.7)

      
    print(myplot)
    
  }

```

